302 lines
11 KiB
Python
302 lines
11 KiB
Python
"""
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HNSW-specific embedding server
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"""
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import argparse
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import json
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import logging
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import os
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import sys
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import threading
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import time
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from pathlib import Path
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from typing import Optional
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import msgpack
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import numpy as np
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import zmq
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# Set up logging based on environment variable
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LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
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logger = logging.getLogger(__name__)
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# Force set logger level (don't rely on basicConfig in subprocess)
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log_level = getattr(logging, LOG_LEVEL, logging.WARNING)
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logger.setLevel(log_level)
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# Ensure we have a handler if none exists
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if not logger.handlers:
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handler = logging.StreamHandler()
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formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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logger.propagate = False
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def create_hnsw_embedding_server(
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passages_file: Optional[str] = None,
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zmq_port: int = 5555,
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model_name: str = "sentence-transformers/all-mpnet-base-v2",
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distance_metric: str = "mips",
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embedding_mode: str = "sentence-transformers",
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):
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"""
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Create and start a ZMQ-based embedding server for HNSW backend.
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Simplified version using unified embedding computation module.
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"""
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logger.info(f"Starting HNSW server on port {zmq_port} with model {model_name}")
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logger.info(f"Using embedding mode: {embedding_mode}")
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# Add leann-core to path for unified embedding computation
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current_dir = Path(__file__).parent
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leann_core_path = current_dir.parent.parent / "leann-core" / "src"
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sys.path.insert(0, str(leann_core_path))
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try:
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from leann.api import PassageManager
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from leann.embedding_compute import compute_embeddings
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logger.info("Successfully imported unified embedding computation module")
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except ImportError as e:
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logger.error(f"Failed to import embedding computation module: {e}")
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return
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finally:
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sys.path.pop(0)
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# Check port availability
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import socket
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def check_port(port):
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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return s.connect_ex(("localhost", port)) == 0
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if check_port(zmq_port):
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logger.error(f"Port {zmq_port} is already in use")
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return
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# Only support metadata file, fail fast for everything else
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if not passages_file or not passages_file.endswith(".meta.json"):
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raise ValueError("Only metadata files (.meta.json) are supported")
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# Load metadata to get passage sources
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with open(passages_file) as f:
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meta = json.load(f)
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# Let PassageManager handle path resolution uniformly
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passages = PassageManager(meta["passage_sources"], metadata_file_path=passages_file)
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logger.info(
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f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
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)
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def zmq_server_thread():
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"""ZMQ server thread"""
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context = zmq.Context()
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socket = context.socket(zmq.REP)
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socket.bind(f"tcp://*:{zmq_port}")
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logger.info(f"HNSW ZMQ server listening on port {zmq_port}")
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socket.setsockopt(zmq.RCVTIMEO, 300000)
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socket.setsockopt(zmq.SNDTIMEO, 300000)
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while True:
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try:
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message_bytes = socket.recv()
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logger.debug(f"Received ZMQ request of size {len(message_bytes)} bytes")
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e2e_start = time.time()
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request_payload = msgpack.unpackb(message_bytes)
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# Handle direct text embedding request
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if isinstance(request_payload, list) and len(request_payload) > 0:
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# Check if this is a direct text request (list of strings)
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if all(isinstance(item, str) for item in request_payload):
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logger.info(
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f"Processing direct text embedding request for {len(request_payload)} texts in {embedding_mode} mode"
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)
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# Use unified embedding computation (now with model caching)
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embeddings = compute_embeddings(
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request_payload, model_name, mode=embedding_mode
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)
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response = embeddings.tolist()
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socket.send(msgpack.packb(response))
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e2e_end = time.time()
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logger.info(f"⏱️ Text embedding E2E time: {e2e_end - e2e_start:.6f}s")
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continue
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# Handle distance calculation requests
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if (
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isinstance(request_payload, list)
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and len(request_payload) == 2
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and isinstance(request_payload[0], list)
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and isinstance(request_payload[1], list)
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):
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node_ids = request_payload[0]
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query_vector = np.array(request_payload[1], dtype=np.float32)
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logger.debug("Distance calculation request received")
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logger.debug(f" Node IDs: {node_ids}")
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logger.debug(f" Query vector dim: {len(query_vector)}")
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# Get embeddings for node IDs
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texts = []
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for nid in node_ids:
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try:
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passage_data = passages.get_passage(str(nid))
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txt = passage_data["text"]
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texts.append(txt)
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except KeyError:
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logger.error(f"Passage ID {nid} not found")
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raise RuntimeError(f"FATAL: Passage with ID {nid} not found")
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except Exception as e:
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logger.error(f"Exception looking up passage ID {nid}: {e}")
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raise
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# Process embeddings
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embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
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logger.info(
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f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
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)
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# Calculate distances
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if distance_metric == "l2":
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distances = np.sum(
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np.square(embeddings - query_vector.reshape(1, -1)), axis=1
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)
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else: # mips or cosine
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distances = -np.dot(embeddings, query_vector)
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response_payload = distances.flatten().tolist()
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response_bytes = msgpack.packb([response_payload], use_single_float=True)
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logger.debug(f"Sending distance response with {len(distances)} distances")
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socket.send(response_bytes)
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e2e_end = time.time()
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logger.info(f"⏱️ Distance calculation E2E time: {e2e_end - e2e_start:.6f}s")
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continue
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# Standard embedding request (passage ID lookup)
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if (
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not isinstance(request_payload, list)
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or len(request_payload) != 1
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or not isinstance(request_payload[0], list)
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):
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logger.error(
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f"Invalid MessagePack request format. Expected [[ids...]] or [texts...], got: {type(request_payload)}"
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)
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socket.send(msgpack.packb([[], []]))
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continue
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node_ids = request_payload[0]
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logger.debug(f"Request for {len(node_ids)} node embeddings")
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# Look up texts by node IDs
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texts = []
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for nid in node_ids:
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try:
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passage_data = passages.get_passage(str(nid))
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txt = passage_data["text"]
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if not txt:
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raise RuntimeError(f"FATAL: Empty text for passage ID {nid}")
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texts.append(txt)
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except KeyError:
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raise RuntimeError(f"FATAL: Passage with ID {nid} not found")
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except Exception as e:
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logger.error(f"Exception looking up passage ID {nid}: {e}")
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raise
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# Process embeddings
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embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
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logger.info(
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f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
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)
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# Serialization and response
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if np.isnan(embeddings).any() or np.isinf(embeddings).any():
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logger.error(
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f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..."
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)
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raise AssertionError()
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hidden_contiguous_f32 = np.ascontiguousarray(embeddings, dtype=np.float32)
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response_payload = [
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list(hidden_contiguous_f32.shape),
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hidden_contiguous_f32.flatten().tolist(),
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]
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response_bytes = msgpack.packb(response_payload, use_single_float=True)
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socket.send(response_bytes)
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e2e_end = time.time()
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logger.info(f"⏱️ ZMQ E2E time: {e2e_end - e2e_start:.6f}s")
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except zmq.Again:
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logger.debug("ZMQ socket timeout, continuing to listen")
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continue
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except Exception as e:
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logger.error(f"Error in ZMQ server loop: {e}")
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import traceback
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traceback.print_exc()
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socket.send(msgpack.packb([[], []]))
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zmq_thread = threading.Thread(target=zmq_server_thread, daemon=True)
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zmq_thread.start()
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logger.info(f"Started HNSW ZMQ server thread on port {zmq_port}")
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# Keep the main thread alive
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try:
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while True:
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time.sleep(1)
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except KeyboardInterrupt:
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logger.info("HNSW Server shutting down...")
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return
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if __name__ == "__main__":
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import signal
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import sys
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def signal_handler(sig, frame):
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logger.info(f"Received signal {sig}, shutting down gracefully...")
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sys.exit(0)
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# Register signal handlers for graceful shutdown
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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parser = argparse.ArgumentParser(description="HNSW Embedding service")
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parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")
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parser.add_argument(
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"--passages-file",
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type=str,
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help="JSON file containing passage ID to text mapping",
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)
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parser.add_argument(
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"--model-name",
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type=str,
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default="sentence-transformers/all-mpnet-base-v2",
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help="Embedding model name",
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)
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parser.add_argument(
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"--distance-metric", type=str, default="mips", help="Distance metric to use"
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)
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parser.add_argument(
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"--embedding-mode",
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type=str,
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default="sentence-transformers",
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choices=["sentence-transformers", "openai", "mlx", "ollama"],
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help="Embedding backend mode",
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)
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args = parser.parse_args()
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# Create and start the HNSW embedding server
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create_hnsw_embedding_server(
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passages_file=args.passages_file,
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zmq_port=args.zmq_port,
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model_name=args.model_name,
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distance_metric=args.distance_metric,
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embedding_mode=args.embedding_mode,
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)
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